Vertical federated learning platform and methods for using same

A computing system for vertical federated machine learning, the system comprising:

• a central system communicatively coupled to a plurality of satellite systems;
• and
• one or more processors coupled to one or more memory devices, wherein the one or more memory devices include instructions which when executed by the one or more processors cause the system to:
• receive, at the central system, first encrypted data based on a first dataset, wherein the first dataset is at a first satellite system;
• receive, at the central system, second encrypted data based on a second dataset, wherein the second dataset is at a second satellite system;
• apply a first transformation to the first encrypted data to generate first transformed data;
• apply a second transformation to the second encrypted data to generate second transformed data;
• identify one or more matching values in the first and second transformed data;
• generate a first set of location indices indicating one or more matching values in the first transformed data;
• generate a second set of location indices indicating one or more matching values in the second transformed data;
• transmit instructions to the first satellite system to train a first local machine learning model using data of the first dataset that is associated with the first set of location indices;
• and
• transmit instructions to the second satellite system to train a second local machine learning model using data of the second dataset that is associated with the second set of location indices.

A computer-readable medium that stores instructions for vertical federated machine learning that, when executed by a computing system, cause the system to:

• receive, at a central system of the computing system, first encrypted data based on a first dataset, wherein the first dataset is at a first satellite system;
• receive, at the central system, second encrypted data based on a second dataset, wherein the second dataset is at a second satellite system;
• apply a first transformation to the first encrypted data to generate first transformed data;
• apply a second transformation to the second encrypted data to generate second transformed data;
• identify one or more matching values in the first and second transformed data;
• generate a first set of location indices indicating one or more matching values in the first transformed data;
• generate a second set of location indices indicating one or more matching values in the second transformed data;
• transmit instructions to the first satellite system to train a first local machine learning model using data of the first dataset that is associated with the first set of location indices;
• and
• transmit instructions to the second satellite system to train a second local machine learning model using data of the second dataset that is associated with the second set of location indices.